Enterprise Knowledge Management: The Data Quality Approach (The Morgan Kaufmann Series in Data Management Systems) (Paperback)

by David Loshin (Author) "Without even realizing it, everyone is affected by poor data quality..." (more)
Key Phrases: data quality rules, data ownership policy, enterprise reference data, New York, Social Security, Postal Service (more...)

Editorial Reviews

Product Description
Today, companies capture and store tremendous amounts of information about every aspect of their business: their customers, partners, vendors, markets, and more. But with the rise in the quantity of information has come a corresponding decrease in its quality--a problem businesses recognize and are working feverishly to solve.
Enterprise Knowledge Management: The Data Quality Approach presents an easily adaptable methodology for defining, measuring, and improving data quality. Author David Loshin begins by presenting an economic framework for understanding the value of data quality, then proceeds to outline data quality rules and domain-and mapping-based approaches to consolidating enterprise knowledge. Written for both a managerial and a technical audience, this book will be indispensable to the growing number of companies committed to wresting every possible advantage from their vast stores of business information.

Key Features
* Expert advice from a highly successful data quality consultant
* The only book on data quality offering the business acumen to appeal to managers and the technical expertise to appeal to IT professionals
* Details the high costs of bad data and the options available to companies that want to transform mere data into true enterprise knowledge
* Presents conceptual and practical information complementing companies' interest in data warehousing, data mining, and knowledge discovery

From the Back Cover

Your company captures and stores tremendous amounts of information about every aspect of its business. But with this rise in the quantity of information has come a corresponding decrease in its quality. Now more than ever, reversing this trend may spell the difference between success and failure. How can you and your organization respond to this challenge?

Enterprise Knowledge Management gives you just what you need: a precise yet adaptable methodology for defining, measuring, and improving data quality and managing business intelligence. This one-of-a-kind book begins by laying out an economic framework for understanding the real business value of data quality. It then outlines rules for measuring data quality and determining where it can and should be improved. Finally, it teaches proven techniques through which you can achieve meaningful advances in the quality of your business data, including domain- and mapping-based consolidation of enterprise knowledge.

Features

• Expert advice from a highly successful data quality consultant.
  
• Rigorously methodical in its approach to the problem and the detailed solution it presents.
  
• Teaches you to measure quality in real business terms and to achieve meaningful, demonstrable improvement.
  
• Uniquely combines business acumen and technical expertise-an indispensable resource for managers and IT professionals alike.
  
• Documents the high costs of bad data and details the options available to any company that wants to transform mere data into true enterprise knowledge.

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Product Details

• Paperback: 491 pages
• Publisher: Morgan Kaufmann; 1st edition (January 22, 2001)
• Language: English
• ISBN-10: 0124558402
• ISBN-13: 978-0124558403
• Product Dimensions: 9.2 x 7.3 x 1.3 inches
• Shipping Weight: 2.4 pounds (View shipping rates and policies)
• Average Customer Review: 4.5 out of 5 stars See all reviews (8 customer reviews)
• Amazon.com Sales Rank: #474,897 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

20 of 20 people found the following review helpful:

5.0 out of 5 stars Author's Comments, February 2, 2001

By	David Loshin "Business Intelligence Thought L... (Silver Spring, MD) - See all my reviews (REAL NAME)

Poor data quality has a profound effect on our everyday lives - consider the 2000 Presidential election and the Florida recount nightmare. Yet, the extent of poor data quality can be effectively measured and therefore, controlled, when we apply process management, technology, and good old common sense!

"Bad data" has traditionally been masked in terms of curious anecdotes and curious stories that propagate through an organization. Yet, poor data quality has a serious effect on a company's bottom line, especially when bad data propagates out to the customer via incorrect billing, wrong delivery addresses, public relations nightmares, etc.

In my experience consulting on data management projects, I noticed many patterns associated with data quality problems. In this book, I try to address both the management issues as well as the technical issues associated with the different kinds of problems, and I try to provide a framework for capturing the knowledge embedded in data quality rules and managing those rules as enterprise knowledge.

I provide a breakdown of the dimensions of data quality, and delineate a framework for expressing data quality rules, measuring those rules, and assessing levels of data quality in a "Data Quality Scorecard." This scorecard can then be used as a benchmark and basis for a continuous information quality improvement program.

In addition, we look at how understanding the business rules associated with the use of information throughout an enterprise can enhance the overall value of the enterprise knowledge asset. Integrating business rules in use across the organization is an important step in enhancing the enterprise knowledge resource, and we have found this to be a successful paradigm in knowledge management applications deployed with our customers.

Data quality problems are widespread, menacing, and can cause serious operational and strategic problems in any organization. By reading my book, I hope to expose some of the critical issues associated with poor data quality and to demonstrate that by fixing the root of data quality problems, organizations can reduce costs due to error detection, correction, and rework, and increase profits by making strategic use of high quality information.

19 of 19 people found the following review helpful:

5.0 out of 5 stars Excellent Methodology!, August 21, 2001

By	Data Quality (Silver Spring, MD) - See all my reviews

I am a consultant in the area of knowledge management and data modeling, and I have read all the major books on the topic of data quality, and this book is, by far, the best treatement of the subject.

Enterprise Knowledge Management is a great handbook for both the manager and the practitioner - Loshin deals with the personal and political aspects of data ownership, buildingan ROI model for data cleansing, and a concise methodology about how to measure levels of data quality.

I have heard speeches by a handful of the major speakers in the area, and my impression is that they are willing to tell you to go and measure data quality, or to talk about data quality issues, but they would be hard-pressed to actually solve the problems. From reading this book, it is clear that Loshin is an expert in this area, and that he has not only dealt with the high level aspects of data management but also has experience in the trenches.

This book is perfect for both manager and technical people dealing with data warehousing or data migration projects.

10 of 11 people found the following review helpful:

5.0 out of 5 stars Data Quality in the Real World, February 5, 2003

By	Glenn Rutz (Elmhurst, IL United States) - See all my reviews

As a data warehouse practitioner for over 12 years, I was recently challenged at my current employer to help assemble a global data quality team and process. Having done much of the work before on a piecemeal basis, we made steady progress.

When I received my copy of "Enterprise Knowledge Management," I found two important things:
1. We were definitely on the right track, and
2. There were some things we had missed.

David Loshin has put together an excellent field guide to all aspects of data quality. It is very easy to understand, and contains practical, effective suggestions. Most importantly, it is a true "soup to nuts" guide to data quality. There is very little that you might need to improve your company's "knowledge quotient" that you will not find here.

I have heartily recommended this book to a number of people when asked about data warehousing and data quality. You'll not find a better handbook anywhere.